Worst‐case behavior of simple sequencing rules in flow shop scheduling with general position‐dependent learning effects

Worst‐case behavior of simple sequencing rules in flow shop scheduling with general position‐dependent learning effects

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Article ID: iaor201111678
Volume: 191
Issue: 1
Start Page Number: 155
End Page Number: 169
Publication Date: Nov 2011
Journal: Annals of Operations Research
Authors: ,
Keywords: learning, production, scheduling, allocation: resources, networks: flow, adaptive processes, combinatorial optimization, programming: multiple criteria, heuristics
Abstract:

A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with a general position‐dependent learning effects. By the general position‐dependent learning effects, we mean that the actual processing time of a job is defined by a general non‐increasing function of its scheduled position. The objective is to minimize one of the five regular performance criteria, namely, the total completion time, the makespan, the total weighted completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single machine scheduling problems. We also analyze the worst‐case bound of our heuristic algorithms.

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